Diamonds_Problem_Set
Problem Set 1 - Exploring 1 Variable
Exploring Diamond Price Distribution
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## Attaching package: 'dplyr'## The following object is masked from 'package:gridExtra':
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## combine## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union## Classes 'tbl_df', 'tbl' and 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...##
## D E F G H I J
## 6775 9797 9542 11292 8304 5422 2808## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 5222 rows containing non-finite values (stat_bin).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 33930 rows containing non-finite values (stat_bin).## Warning: Removed 1 rows containing missing values (geom_bar).
## [1] 1729## [1] 0## [1] 1656## Saving 7 x 5 in image## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 33930 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).Exploring Diamond Price Distribution by Cut
## diamonds$cut: Fair
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 337 2050 3282 4359 5206 18574
## --------------------------------------------------------
## diamonds$cut: Good
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 327 1145 3050 3929 5028 18788
## --------------------------------------------------------
## diamonds$cut: Very Good
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 336 912 2648 3982 5373 18818
## --------------------------------------------------------
## diamonds$cut: Premium
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 326 1046 3185 4584 6296 18823
## --------------------------------------------------------
## diamonds$cut: Ideal
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 326 878 1810 3458 4678 18806## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Saving 7 x 5 in image
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Exploring Diamond Price/Carat Distribution by Cut
## diamonds$cut: Fair
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.063 7.917 8.146 8.156 8.415 9.297
## --------------------------------------------------------
## diamonds$cut: Good
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.985 7.781 8.192 8.150 8.474 9.676
## --------------------------------------------------------
## diamonds$cut: Very Good
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.038 7.754 8.190 8.177 8.520 9.789
## --------------------------------------------------------
## diamonds$cut: Premium
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.958 7.860 8.233 8.238 8.580 9.746
## --------------------------------------------------------
## diamonds$cut: Ideal
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.011 7.806 8.104 8.157 8.469 9.746## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Exploring Diamond Price and Price/Carat Distribution by Color
## diamonds$color: D
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.028 7.806 8.135 8.169 8.466 9.789
## --------------------------------------------------------
## diamonds$color: E
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.983 7.796 8.088 8.139 8.414 9.589
## --------------------------------------------------------
## diamonds$color: F
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.063 7.858 8.159 8.211 8.507 9.537
## --------------------------------------------------------
## diamonds$color: G
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.038 7.839 8.158 8.210 8.613 9.430
## --------------------------------------------------------
## diamonds$color: H
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.958 7.782 8.248 8.187 8.542 9.229
## --------------------------------------------------------
## diamonds$color: I
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.049 7.760 8.237 8.175 8.556 9.148
## --------------------------------------------------------
## diamonds$color: J
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.985 7.849 8.237 8.147 8.503 9.065## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
## D E F G H I J
## 6775 9797 9542 11292 8304 5422 2808## diamonds$color: D
## [1] 0.6577948
## --------------------------------------------------------
## diamonds$color: E
## [1] 0.6578667
## --------------------------------------------------------
## diamonds$color: F
## [1] 0.7365385
## --------------------------------------------------------
## diamonds$color: G
## [1] 0.7711902
## --------------------------------------------------------
## diamonds$color: H
## [1] 0.9117991
## --------------------------------------------------------
## diamonds$color: I
## [1] 1.026927
## --------------------------------------------------------
## diamonds$color: J
## [1] 1.162137##
## D E F G H I J
## (0,0.5] 2898 4276 3418 4147 2415 1316 462
## (0.5,1] 2556 3629 3523 3426 2252 1426 694
## (1,1.5] 1064 1476 2040 2800 2409 1436 835
## (1.5,2] 213 338 440 679 764 685 434
## (2,2.5] 40 73 117 228 429 526 350
## (2.5,3] 3 4 3 11 29 20 24
## (3,6] 1 1 1 1 6 13 9## [1] 96.00667## [1] 6775##
## (0,0.5] (0.5,1] (1,1.5] (1.5,2] (2,2.5] (2.5,3] (3,6]
## 2898 2556 1064 213 40 3 1##
## D E F G H I J
## 44 78 121 240 464 559 383## Saving 7 x 5 in imageInterquartile range
## 0% 25% 50% 75% 100%
## 357.0 911.0 1838.0 4213.5 18693.0## 0% 25% 50% 75% 100%
## 335.0 1860.5 4234.0 7695.0 18710.0## [1] 3302.5## [1] 5834.5Price per carat by color boxplot
## Saving 7 x 5 in imageWeight distribution
Problem Set 2 - Exploring 2 Variables
Price and Volume
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `geom_smooth()` using method = 'gam'
## [1] 0.8844352## [1] 0.8654209## [1] 0.8612494
## 0% 25% 50% 75% 100%
## 43.0 61.0 61.8 62.5 79.0## [1] -0.0106474
## [1] 0.9235455## `geom_smooth()` using method = 'gam'
## # A tibble: 273 x 6
## carat mean_price median_price min_price max_price `n()`
## <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 0.20 365.1667 367 345 367 12
## 2 0.21 380.2222 386 326 394 9
## 3 0.22 391.4000 404 337 470 5
## 4 0.23 486.1433 498 326 688 293
## 5 0.24 505.1850 491 336 963 254
## 6 0.25 550.9245 548 357 1186 212
## 7 0.26 550.8972 554 337 814 253
## 8 0.27 574.7597 575 361 893 233
## 9 0.28 580.1212 586 360 828 198
## 10 0.29 601.1923 607 334 1776 130
## # ... with 263 more rows## Saving 7 x 5 in image
Problem Set 3 - Exploring 3 Variables
Price by Color and Cut
Price by Table and Cut
## diamonds$cut: Fair
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 49.00 56.00 58.00 59.05 61.00 95.00
## --------------------------------------------------------
## diamonds$cut: Good
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 51.00 56.00 58.00 58.69 61.00 66.00
## --------------------------------------------------------
## diamonds$cut: Very Good
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 44.00 56.00 58.00 57.96 59.00 66.00
## --------------------------------------------------------
## diamonds$cut: Premium
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 51.00 58.00 59.00 58.75 60.00 62.00
## --------------------------------------------------------
## diamonds$cut: Ideal
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 43.00 55.00 56.00 55.95 57.00 63.00Price by Volume and Clarity
Price by Volume and Clarity
Create a scatter plot of the price/carat ratio of diamonds. The variable x should be assigned to cut. The points should be colored by diamond color, and the plot should be faceted by clarity.
